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Aitor Alvarez-Gila - IEEE Xplore Author Profile

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Author details

Aitor Alvarez-Gila

Publications

7

Citations

327

Publications by Year

20172025

Co-Authors:

Omer Faruk AdilCosmin AncutiCodruta O. AncutiBoaz AradJose Antonio Arteche

Show All Co-Authors (113)

Aitor Alvarez-Gila

Affiliation

TECNALIA

Basque Research and Technology Alliance (BRTA)

Parque Tecnologico de Bizkaia, C/ Geldo, Edificio 700, Derio, Bizkaia, (Spain)

Publication Topics

Amount Of Annotated Data,
Annotation Data,
Annotation Efforts,
Atmospheric Absorption,
Automatic Annotation,
Automatic Counter,
Bayesian Model,
Camera Position,
Candidate Detection,
Color Space,
Computer Vision,
Control Condition

Biography

Aitor Alvarez-Gila received the M.Sc. degree in telecommunications engineering from The University of the Basque Country. He is currently pursuing the Ph.D. degree in deep learning techniques Learning and Machine Perception Team (LAMP), Computer Vision Centre (CVC), Universitat Autònoma de Barcelona (UAB). After working for three years as a technology consultant in Madrid and London (Accenture, Management Solutions), from 2008 to 2010, he completed the Erasmus Mundus Master CIMET (Colour in Informatics and Media Technology) in machine vision and colour science, visiting the Universities of Granada, Saint-Etienne, GjØvik and Amsterdam. Since 2010, he has been a Researcher with the Tecnalia’s Computer Vision Group. He has collaborated in proj...

Publications

7

Citations

327

Publications by Year

20172025

Co-Authors:

Omer Faruk Adil
Cosmin Ancuti
Codruta O. Ancuti
Boaz Arad
Jose Antonio Arteche

Show All Co-Authors (113)

Author's Published Works

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Showing 1-25 of 51 results

Conferences (37)

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BokehMe++: Harmonious Fusion of Classical and Neural Rendering for Versatile Bokeh Creation

Juewen Peng;Zhiguo Cao;Xianrui Luo;Ke Xian;Wenfeng Tang;Jianming Zhang;Guosheng Lin

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 3 | Journal Article |

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Despite significant advancements in simulating the bokeh effect of Digital Single Lens Reflex Camera (DSLR) from an all-in-focus image, challenges remain in processing highlight points, preserving boundary details for in-focus objects and processing high-resolution images efficiently. To tackle these issues, we first develop a ray-tracing-based bokeh simulator. An innovative pipeline with weight r...Show More

BokehMe++: Harmonious Fusion of Classical and Neural Rendering for Versatile Bokeh Creation

Juewen Peng;Zhiguo Cao;Xianrui Luo;Ke Xian;Wenfeng Tang;Jianming Zhang;Guosheng Lin

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 3 | Journal Article |

NVDS$^{\mathbf{+}}$+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation

Yiran Wang;Min Shi;Jiaqi Li;Chaoyi Hong;Zihao Huang;Juewen Peng;Zhiguo Cao;Jianming Zhang;Ke Xian;Guosheng Lin

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 1 | Journal Article |

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Video depth estimation aims to infer temporally consistent depth. One approach is to finetune a single-image model on each video with geometry constraints, which proves inefficient and lacks robustness. An alternative is learning to enforce consistency from data, which requires well-designed models and sufficient video depth data. To address both challenges, we introduce NVDS$^{\mathbf{+}}$+ that ...Show More

NVDS$^{\mathbf{+}}$+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation

Yiran Wang;Min Shi;Jiaqi Li;Chaoyi Hong;Zihao Huang;Juewen Peng;Zhiguo Cao;Jianming Zhang;Ke Xian;Guosheng Lin

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Volume: 47, Issue: 1 | Journal Article |

UniHuman: A Unified Model For Editing Human Images in the Wild

Nannan Li;Qing Liu;Krishna Kumar Singh;Yilin Wang;Jianming Zhang;Bryan A. Plummer;Zhe Lin

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Cited by: Papers (3)

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Human image editing includes tasks like changing a person's pose, their clothing, or editing the image according to a text prompt. However, prior work often tackles these tasks separately, overlooking the benefit of mutual reinforcement from learning them jointly. In this paper, we propose UniHuman, a unified model that addresses multiple facets of human image editing in real-world settings. To en...Show More

UniHuman: A Unified Model For Editing Human Images in the Wild

Nannan Li;Qing Liu;Krishna Kumar Singh;Yilin Wang;Jianming Zhang;Bryan A. Plummer;Zhe Lin

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Structure-Guided Image Completion With Image-Level and Object-Level Semantic Discriminators

Haitian Zheng;Zhe Lin;Jingwan Lu;Scott Cohen;Eli Shechtman;Connelly Barnes;Jianming Zhang;Qing Liu;Sohrab Amirghodsi;Yuqian Zhou;Jiebo Luo

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

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Structure-guided image completion aims to inpaint a local region of an image according to an input guidance map from users. While such a task enables many practical applications for interactive editing, existing methods often struggle to hallucinate realistic object instances in complex natural scenes. Such a limitation is partially due to the lack of semantic-level constraints inside the hole reg...Show More

Structure-Guided Image Completion With Image-Level and Object-Level Semantic Discriminators

Haitian Zheng;Zhe Lin;Jingwan Lu;Scott Cohen;Eli Shechtman;Connelly Barnes;Jianming Zhang;Qing Liu;Sohrab Amirghodsi;Yuqian Zhou;Jiebo Luo

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

ViTA: Video Transformer Adaptor for Robust Video Depth Estimation

Ke Xian;Juewen Peng;Zhiguo Cao;Jianming Zhang;Guosheng Lin

IEEE Transactions on Multimedia

Year: 2024 | Volume: 26 | Journal Article |

Cited by: Papers (1)

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Depth information plays a pivotal role in numerous computer vision applications, including autonomous driving, 3D reconstruction, and 3D content generation. When deploying depth estimation models in practical applications, it is essential to ensure that the models have strong generalization capabilities. However, existing depth estimation methods primarily concentrate on robust single-image depth ...Show More

ViTA: Video Transformer Adaptor for Robust Video Depth Estimation

Ke Xian;Juewen Peng;Zhiguo Cao;Jianming Zhang;Guosheng Lin

IEEE Transactions on Multimedia

Year: 2024 | Volume: 26 | Journal Article |

Neural Video Depth Stabilizer

Yiran Wang;Min Shi;Jiaqi Li;Zihao Huang;Zhiguo Cao;Jianming Zhang;Ke Xian;Guosheng Lin

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (10)

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Video depth estimation aims to infer temporally consistent depth. Some methods achieve temporal consistency by finetuning a single-image depth model during test time using geometry and re-projection constraints, which is inefficient and not robust. An alternative approach is to learn how to enforce temporal consistency from data, but this requires well-designed models and sufficient video depth da...Show More

Neural Video Depth Stabilizer

Yiran Wang;Min Shi;Jiaqi Li;Zihao Huang;Zhiguo Cao;Jianming Zhang;Ke Xian;Guosheng Lin

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

LightPainter: Interactive Portrait Relighting with Freehand Scribble

Yiqun Mei;He Zhang;Xuaner Zhang;Jianming Zhang;Zhixin Shu;Yilin Wang;Zijun Wei;Shi Yan;HyunJoon Jung;Vishal M. Patel

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (6)

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Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map. However, these methods are not intuitive for user interaction and lack precise lighting control. We introduce LightPainter, a scribble-based relighting system that allows users to interactively manipulate portrait lighting effect with e...Show More

LightPainter: Interactive Portrait Relighting with Freehand Scribble

Yiqun Mei;He Zhang;Xuaner Zhang;Jianming Zhang;Zhixin Shu;Yilin Wang;Zijun Wei;Shi Yan;HyunJoon Jung;Vishal M. Patel

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Single View Scene Scale Estimation using Scale Field

Byeong-Uk Lee;Jianming Zhang;Yannick Hold-Geoffroy;In So Kweon

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

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In this paper, we propose a single image scale estimation method based on a novel scale field representation. A scale field defines the local pixel-to-metric conversion ratio along the gravity direction on all the ground pixels. This representation resolves the ambiguity in camera parameters, allowing us to use a simple yet effective way to collect scale annotations on arbitrary images from human ...Show More

Single View Scene Scale Estimation using Scale Field

Byeong-Uk Lee;Jianming Zhang;Yannick Hold-Geoffroy;In So Kweon

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

ObjectStitch: Object Compositing with Diffusion Model

Yizhi Song;Zhifei Zhang;Zhe Lin;Scott Cohen;Brian Price;Jianming Zhang;Soo Ye Kim;Daniel Aliaga

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (12)

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Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results. Furthermore, annotating training data pairs for compositing requires substantial manual effort from professionals, and is hardly scalable. Thus, with the recent advances in generat...Show More

ObjectStitch: Object Compositing with Diffusion Model

Yizhi Song;Zhifei Zhang;Zhe Lin;Scott Cohen;Brian Price;Jianming Zhang;Soo Ye Kim;Daniel Aliaga

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing

Yichen Sheng;Jianming Zhang;Julien Philip;Yannick Hold-Geoffroy;Xin Sun;He Zhang;Lu Ling;Bedrich Benes

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (7)

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Lighting effects such as shadows or reflections are key in making synthetic images realistic and visually appealing. To generate such effects, traditional computer graphics uses a physically-based renderer along with 3D geometry. To compensate for the lack of geometry in 2D Image compositing, recent deep learning-based approaches introduced a pixel height representation to generate soft shadows an...Show More

PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing

Yichen Sheng;Jianming Zhang;Julien Philip;Yannick Hold-Geoffroy;Xin Sun;He Zhang;Lu Ling;Bedrich Benes

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

SceneComposer: Any-Level Semantic Image Synthesis

Yu Zeng;Zhe Lin;Jianming Zhang;Qing Liu;John Collomosse;Jason Kuen;Vishal M. Patel

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (18)

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We propose a new framework for conditional image synthesis from semantic layouts of any precision levels, ranging from pure text to a 2D semantic canvas with precise shapes. More specifically, the input layout consists of one or more semantic regions with free-form text descriptions and adjustable precision levels, which can be set based on the desired controllability. The framework naturally redu...Show More

SceneComposer: Any-Level Semantic Image Synthesis

Yu Zeng;Zhe Lin;Jianming Zhang;Qing Liu;John Collomosse;Jason Kuen;Vishal M. Patel

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Perspective Fields for Single Image Camera Calibration

Linyi Jin;Jianming Zhang;Yannick Hold-Geoffroy;Oliver Wang;Kevin Blackburn-Matzen;Matthew Sticha;David F. Fouhey

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (11)

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Geometric camera calibration is often required for applications that understand the perspective of the image. We propose Perspective Fields as a representation that models the local perspective properties of an image. Perspective Fields contain per-pixel information about the camera view, parameterized as an Up-vector and a Latitude value. This representation has a number of advantages; it makes m...Show More

Perspective Fields for Single Image Camera Calibration

Linyi Jin;Jianming Zhang;Yannick Hold-Geoffroy;Oliver Wang;Kevin Blackburn-Matzen;Matthew Sticha;David F. Fouhey

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

3D Cinemagraphy from a Single Image

Xingyi Li;Zhiguo Cao;Huiqiang Sun;Jianming Zhang;Ke Xian;Guosheng Lin

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (6)

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We present 3D Cinemagraphy, a new technique that mar-ries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion. We empirically find that naively combining existing 2D image animation and 3D photography methods leads to obvious artifacts or inconsistent animation. Our key insight is...Show More

3D Cinemagraphy from a Single Image

Xingyi Li;Zhiguo Cao;Huiqiang Sun;Jianming Zhang;Ke Xian;Guosheng Lin

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Towards Accurate Reconstruction of 3D Scene Shape From A Single Monocular Image

Wei Yin;Jianming Zhang;Oliver Wang;Simon Niklaus;Simon Chen;Yifan Liu;Chunhua Shen

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 5 | Journal Article |

Cited by: Papers (26)

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Despite significant progress made in the past few years, challenges remain for depth estimation using a single monocular image. First, it is nontrivial to train a metric-depth prediction model that can generalize well to diverse scenes mainly due to limited training data. Thus, researchers have built large-scale relative depth datasets that are much easier to collect. However, existing relative de...Show More

Towards Accurate Reconstruction of 3D Scene Shape From A Single Monocular Image

Wei Yin;Jianming Zhang;Oliver Wang;Simon Niklaus;Simon Chen;Yifan Liu;Chunhua Shen

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 5 | Journal Article |

Semantic Layout Manipulation With High-Resolution Sparse Attention

Haitian Zheng;Zhe Lin;Jingwan Lu;Scott Cohen;Jianming Zhang;Ning Xu;Jiebo Luo

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 3 | Journal Article |

Cited by: Papers (6)

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We tackle the problem of semantic image layout manipulation, which aims to manipulate an input image by editing its semantic label map. A core problem of this task is how to transfer visual details from the input images to the new semantic layout while making the resulting image visually realistic. Recent work on learning cross-domain correspondence has shown promising results for global layout tr...Show More

Semantic Layout Manipulation With High-Resolution Sparse Attention

Haitian Zheng;Zhe Lin;Jingwan Lu;Scott Cohen;Jianming Zhang;Ning Xu;Jiebo Luo

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 3 | Journal Article |

BokehMe: When Neural Rendering Meets Classical Rendering

Juewen Peng;Zhiguo Cao;Xianrui Luo;Hao Lu;Ke Xian;Jianming Zhang

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (29)

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We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer with a classical physically motivated renderer. Given a single image and a potentially imperfect disparity map, BokehMe generates high-resolution photo-realistic bokeh effects with adjustable blur size, focal plane, and aperture shape. To this end, we analyze the errors from the classical scattering-based method ...Show More

BokehMe: When Neural Rendering Meets Classical Rendering

Juewen Peng;Zhiguo Cao;Xianrui Luo;Hao Lu;Ke Xian;Jianming Zhang

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Lite Vision Transformer with Enhanced Self-Attention

Chenglin Yang;Yilin Wang;Jianming Zhang;He Zhang;Zijun Wei;Zhe Lin;Alan Yuille

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (76)

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Despite the impressive representation capacity of vision transformer models, current light-weight vision transformer models still suffer from inconsistent and incorrect dense predictions at local regions. We suspect that the power of their self-attention mechanism is limited in shallower and thinner networks. We propose Lite Vision Transformer (LVT), a novel light-weight transformer network with t...Show More

Lite Vision Transformer with Enhanced Self-Attention

Chenglin Yang;Yilin Wang;Jianming Zhang;He Zhang;Zijun Wei;Zhe Lin;Alan Yuille

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Layered Depth Refinement with Mask Guidance

Soo Ye Kim;Jianming Zhang;Simon Niklaus;Yifei Fan;Simon Chen;Zhe Lin;Munchurl Kim

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (5)

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Depth maps are used in a wide range of applications from 3D rendering to 2D image effects such as Bokeh. However, those predicted by single image depth estimation (SIDE) models often fail to capture isolated holes in objects and/or have inaccurate boundary regions. Meanwhile, high-quality masks are much easier to obtain, using commercial auto-masking tools or off-the-shelf methods of segmentation ...Show More

Layered Depth Refinement with Mask Guidance

Soo Ye Kim;Jianming Zhang;Simon Niklaus;Yifei Fan;Simon Chen;Zhe Lin;Munchurl Kim

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

SSH: A Self-Supervised Framework for Image Harmonization

Yifan Jiang;He Zhang;Jianming Zhang;Yilin Wang;Zhe Lin;Kalyan Sunkavalli;Simon Chen;Sohrab Amirghodsi;Sarah Kong;Zhangyang Wang

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

Cited by: Papers (43)

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Image harmonization aims to improve the quality of image compositing by matching the "appearance" (e.g., color tone, brightness and contrast) between foreground and background images. However, collecting large-scale annotated datasets for this task requires complex professional retouching. Instead, we propose a novel Self-Supervised Harmonization framework (SSH) that can be trained using just "fre...Show More

SSH: A Self-Supervised Framework for Image Harmonization

Yifan Jiang;He Zhang;Jianming Zhang;Yilin Wang;Zhe Lin;Kalyan Sunkavalli;Simon Chen;Sohrab Amirghodsi;Sarah Kong;Zhangyang Wang

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

Learning to Recover 3D Scene Shape from a Single Image

Wei Yin;Jianming Zhang;Oliver Wang;Simon Niklaus;Long Mai;Simon Chen;Chunhua Shen

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (123)

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Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length. We investigate this problem in detail, and propose a two-stage framework that f...Show More

Learning to Recover 3D Scene Shape from a Single Image

Wei Yin;Jianming Zhang;Oliver Wang;Simon Niklaus;Long Mai;Simon Chen;Chunhua Shen

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

SSN: Soft Shadow Network for Image Compositing

Yichen Sheng;Jianming Zhang;Bedrich Benes

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (23)

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We introduce an interactive Soft Shadow Network (SSN) to generates controllable soft shadows for image compositing. SSN takes a 2D object mask as input and thus is agnostic to image types such as painting and vector art. An environment light map is used to control the shadow’s characteristics, such as angle and softness. SSN employs an Ambient Occlusion Prediction module to predict an intermediate...Show More

SSN: Soft Shadow Network for Image Compositing

Yichen Sheng;Jianming Zhang;Bedrich Benes

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Multimodal Contrastive Training for Visual Representation Learning

Xin Yuan;Zhe Lin;Jason Kuen;Jianming Zhang;Yilin Wang;Michael Maire;Ajinkya Kale;Baldo Faieta

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (80)

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We develop an approach to learning visual representations that embraces multimodal data, driven by a combination of intra- and inter-modal similarity preservation objectives. Unlike existing visual pre-training methods, which solve a proxy prediction task in a single domain, our method exploits intrinsic data properties within each modality and semantic information from cross-modal correlation sim...Show More

Multimodal Contrastive Training for Visual Representation Learning

Xin Yuan;Zhe Lin;Jason Kuen;Jianming Zhang;Yilin Wang;Michael Maire;Ajinkya Kale;Baldo Faieta

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Mask Guided Matting via Progressive Refinement Network

Qihang Yu;Jianming Zhang;He Zhang;Yilin Wang;Zhe Lin;Ning Xu;Yutong Bai;Alan Yuille

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (76)

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We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance. MG Matting leverages a network (PRN) design which encourages the matting model to provide self-guidance to progressively refine the uncertain regions through the decoding process. A series of guidance mask perturbation operations are also introduced in the training to further enhance its r...Show More

Mask Guided Matting via Progressive Refinement Network

Qihang Yu;Jianming Zhang;He Zhang;Yilin Wang;Zhe Lin;Ning Xu;Yutong Bai;Alan Yuille

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Explainable Deep Classification Models for Domain Generalization

Andrea Zunino;Sarah Adel Bargal;Riccardo Volpi;Mehrnoosh Sameki;Jianming Zhang;Stan Sclaroff;Vittorio Murino;Kate Saenko

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

Cited by: Papers (26)

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Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object classification, but as a consequence, suffers no perceptible accuracy degradation. Explanations are defined as regions of visual evidence upon which a deep classification network makes a decision. This is represented in ...Show More

Explainable Deep Classification Models for Domain Generalization

Andrea Zunino;Sarah Adel Bargal;Riccardo Volpi;Mehrnoosh Sameki;Jianming Zhang;Stan Sclaroff;Vittorio Murino;Kate Saenko

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2021 | Conference Paper |

Deep Image Compositing

He Zhang;Jianming Zhang;Federico Perazzi;Zhe Lin;Vishal M. Patel

2021 IEEE Winter Conference on Applications of Computer Vision (WACV)

Year: 2021 | Conference Paper |

Cited by: Papers (26)

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Image compositing is a task of combining regions from different images to compose a new image. A common use case is background replacement of portrait images. To obtain high quality composites, professionals typically manually perform multiple editing steps such as segmentation, matting and foreground color decontamination, which is very time consuming even with sophisticated photo editing tools. ...Show More

Deep Image Compositing

He Zhang;Jianming Zhang;Federico Perazzi;Zhe Lin;Vishal M. Patel

2021 IEEE Winter Conference on Applications of Computer Vision (WACV)

Year: 2021 | Conference Paper |

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